The research of methods to reduce CO2 emissions into the atmosphere has led to formation of new thermodynamic cycles in which oxygen is separated from the air before combustion. Fuel, pure oxygen and some recirculating substances, from which it is easy to separate CO2 formed during the combustion, are fed into the combustion chamber. Usually, CO2, H2O or a mixture of thereof are used in the form of recirculated flue gas. The parameters in such cycles are chosen at different points in the cycle, where the working fluid can be in liquid, gaseous or supercritical states. The computational study of such cycles requires a convenient presentation of the thermophysical properties of different substances that can be part of the working fluid in a wide range of parameters. The aim of this work is to develop a data array and a computational module (spreadsheet) considering the dependence of the basic thermophysical properties of various substances. A conversion method of variables that allowed the formation of a compact interpolation grid with minimal loss of accuracy during subsequent interpolation was proposed, where the use of integers for the nodal values of the independent variables saved computational resources during interpolation significantly.
<p>Surface water bodies serve a critical role in preserving ecological systems and maintaining biodiversity. Anthropogenic eutrophication of fresh water ecosystems is one of the main causes of surface water quality degradation. Excessive nutrient loading to freshwater bodies is a driving cause of water quality impairments worldwide. Accurately estimating riverine nutrient loads remains an imperative step towards mitigating and managing impairments. Yet, load estimation is often hindered by the sporadic and infrequent monitoring of nutrient concentrations. Several modelling approaches have been proposed and implemented over the years to estimate pollutant loads; yet most suffer from biases and/or from their capabilities to transparently quantify uncertainties. In this work, we propose a spatio-temporal Bayesian hierarchical ratio-estimator model to predict the annual total phosphorus loads between 2005 and 2020 for six intensively monitored watersheds discharging in Lake Erie and the Ohio River-USA. The integration of higher-level Land-Use-Land-Cover predictors proved successful in capturing inter-station variabilities in phosphorus loading. Meanwhile, accounting for annual climatic variability partially helped explain temporal changes in the flow-weighted nutrient concentrations across the six watersheds. The performance of the model was tested against different levels of data censorship. Results showed that under a weekly sampling program, the load estimates from the proposed Bayesian Hierarchical spatio-temporal model were within -19 and 31 % (mean difference of 0.3% across stations and years) from the true loads calculated for years with uninterrupted concentration measurements. Predictions from traditional load estimation methods were found to vary between -56% and 73% from the true loads. Meanwhile, failing to account for the spatio-temporal hierarchical structure of the proposed model, either by adopting a completely pooled or an unpooled model, resulted in a significant drop in the accuracy of the predicted loads and inflated the associated uncertainties.</p>
The article discusses the issues of reducing the size of the cooling unit of the antenna of a radar station by improving the gas-dynamic processes occurring in the air-cooling unit. The results of the experimental studies of the gas flow in a plate-fin heat exchanger, being blown by one axial fan are presented. The feasibility of changing the number of axial fans for organizing a more uniform flow around the heat-exchange surfaces has been determined by calculation and theoretical methods. The calculation results are confirmed by experimental studies of the air flow in the segment of the heat exchanger, which is provided by a smaller fan.
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